arXiv:2606.24990v1 Announce Type: new Abstract: Reinforcement Learning (RL) has become a powerful paradigm for de novo molecular design, enabling Chemical Language Models (CLMs) to navigate and explore the chemical space while optimizing specific desired properties. However, the existing RL frameworks treat all scoring functions as deterministic oracles, neglecting the inherent uncertainty attached to the predictions of the different molecular properties. This can lead to the exploration of highly-uncertain regions of the chemical space, focusing on the generation of highly scored molecules wh
The increasing sophistication of AI models in scientific discovery necessitates addressing inherent uncertainties to ensure robust and reliable outcomes.
Improving uncertainty quantification in AI-driven molecular design will accelerate the discovery of novel compounds with desired properties, impacting fields like medicine and materials science.
Reinforcement learning frameworks in chemical language models will evolve to explicitly account for and optimize against prediction uncertainties, leading to more targeted and efficient exploration of chemical space.
- · Pharmaceutical R&D
- · Material science companies
- · AI-driven drug discovery platforms
- · Traditional drug discovery methods
- · Companies relying on deterministic molecular modeling
More efficient and reliable discovery of new molecules for drugs and materials.
Reduced R&D costs and accelerated time-to-market for new chemical products.
Potential for designing entirely new classes of materials or therapies previously thought impossible.
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Read at arXiv cs.LG